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Idiap/
gated-deltanet-nsa-0.4B-10B

Name: gated-deltanet-nsa-0.4B-10B
Availability: InStock

safetensors

hybrid_gated_deltanet

2510.20787

mit

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gated-deltanet-nsa-0.4B-10B

Gated DeltaNet + native sparse attention (0.4B params, 10B tokens)

Overview

Training: gated-deltanet-nsa-0.4B-10B was trained on FineWeb-Edu, which is realeased under ODC-By v1.0
Parameters: 0.4B
Task: Language modeling
Framework: HuggingFace, flash-linear-attention
Output structure: [batch_size, sequence_length, num_logits]

Performance

Various; available in paper

Running Code

Minimal code to instantiate the model and perform inference:

python

# Requires flash-linear-attention (https://github.com/fla-org/flash-linear-attention)
import fla
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained(path_to_model).cuda()
tokenizer = AutoTokenizer.from_pretrained(path_to_model).cuda()
input_ids = tokenizer("All human beings are", return_tensors="pt").input_ids
outputs = model.generate(input_ids, max_length=15)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

License

Gated DeltaNet is released under MIT License

Citation

If you find our work useful, please cite the following publication:

bibtex

@misc{he_alleviating_2025,
    title = {Alleviating {Forgetfulness} of {Linear} {Attention} by {Hybrid} {Sparse} {Attention} and {Contextualized} {Learnable} {Token} {Eviction}},
    url = {http://arxiv.org/abs/2510.20787},
    doi = {10.48550/arXiv.2510.20787},
    publisher = {arXiv},
    author = {He, Mutian and Garner, Philip N.},
    month = oct,
    year = {2025},
    note = {arXiv:2510.20787 [cs]},
}

Idiap/gated-deltanet-nsa-0.4B-10B